Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aws/tools-for-devops-agent --skill sagemaker-ai-ops-reviewgit clone --depth 1 https://github.com/aws/tools-for-devops-agentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aws/tools-for-devops-agent/sagemaker-ai-ops-review)<a href="https://agentmods.dev/skills/aws/tools-for-devops-agent/sagemaker-ai-ops-review"><img src="https://agentmods.dev/badge/skills/aws/tools-for-devops-agent/sagemaker-ai-ops-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aws/tools-for-devops-agent/sagemaker-ai-ops-review"><img src="https://agentmods.dev/badge/skills/aws/tools-for-devops-agent/sagemaker-ai-ops-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00166 | $0.03595 |
| Opus 5.5 | $0.00066 | $0.01438 |
| Sonnet 5.5 | $0.00033 | $0.00719 |
| Haiku 4.5 | $0.00017 | $0.00360 |
Grade A, and why
sagemaker-ai-ops-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 14d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Amazon SageMaker AI Operational Review
Run the Amazon SageMaker AI operational review checks against a customer's Amazon SageMaker AI
resources and produce an Amazon SageMaker AI Operational Review report. It evaluates
8 pillars, 20 checks using native AWS APIs (via use_aws). The Best Practices pillar's
recommendations are grounded in the public AWS Well-Architected lenses — see pillar-checks.md.
This is a strict READ-ONLY review: data is collected through native AWS List* /
Describe* control-plane APIs, CloudWatch metric reads, servicequotas:GetServiceQuota,
health:DescribeEvents, and savingsplans:DescribeSavingsPlans. It performs no model
invocations, launches no jobs, and reads no inference payloads.
When to Use
Activate this skill when the user asks to review, audit, or assess an Amazon SageMaker AI workload, check SageMaker AI best-practices posture, or run an Operational Readiness Review (ORR) for SageMaker AI — for one pillar, a subset of checks, or the full set.
The ORR use case is the primary one: run this review before a team deploys a SageMaker AI workload to production, as the readiness gate. Because every finding is severity-ranked and carries a concrete remediation, the report doubles as the pre-production punch list — clear the High and Medium findings, then launch. It is equally suited to a recurring cadence afterwards (weekly or monthly posture review) and to an ad-hoc audit of a newly inherited account.
Pillars and Checks
Run checks grouped by pillar in the order below. Load references/pillar-checks.md for
each check's APIs, logic, thresholds, and output fields.
| Pillar | Checks |
|---|---|
| Security | Check Encryption · SageMaker VPC Check · VPC Configuration Check |
| Performance | SageMaker Endpoint Inference Type · SageMaker Endpoint Latency |
| Cost Optimization | SageMaker Resource Tagging Check · Trainium and Inferentia Usage · Autoscaling Endpoint Check · Sagemaker Savings Plan · Sagemaker Lifecycle Configurations · Sagemaker Inference Recommender Jobs Check · Sagemaker Stale Endpoints Check |
| Service Quotas | Service Quotas Check |
| Resiliency | SageMaker Endpoint Instances · SageMaker Lifecycle Events |
| Operational Excellence | Sagemaker Project Check · Sagemaker Pipeline Check · SageMaker Endpoint Datacapture Enabled Check |
| Sustainability | Domain Region Check |
| Best Practices | Well-Architected Recommendations (SageMaker AI) |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 14d ago First seen · 236 lines · 166 tokens per session scan A 5018d9a64995
sagemaker-ai-ops-review is a skill published in the GitHub repository aws/tools-for-devops-agent (107 stars, last pushed today), licensed Apache-2.0. It adds 166 tokens to every session and 3,595 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-02.
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